Analytical sensors play a key role in many industrial processes and require attention to ensure accurate and reliable performance. In particular, sensors used in applications requiring highly accurate measurements of a process fluid which coats or fouls the sensor, require regular maintenance. Likewise, sensors with a limited life expectancy, such as Oxidation Reduction Potential (ORP), pH or conductivity sensors, also require constant attention. However, persistent maintenance of sensors can impact operational efficiency. At the same time, failure to properly maintain sensors negatively impacts operations potentially resulting in reduced product quality, over/under used reagents and unplanned downtime for cleaning or replacement.
Approaches to this issue have thus far been unsatisfactory. For example, one approach involves using a static maintenance schedule based on a best-historical guess to maintain a given level of pH accuracy. This may be supplemented by using many redundant sensors (three or more) over redundant pairs or a single sensor. This increases the number of probes consumed in a given time period and/or leaves one vulnerable to poor historical guesses or unusually poor pH performance. The result is subpar pH accuracy and/or excess maintenance and labor expense.
Another approach involves using diagnostics on board the individual sensor. This captures certain fail states, such as when the glass of a sensor is cracked or broken. However, this approach does not capture instances where the sensor is functioning, but performance may be less than desired because of the environment in contact with or immediately around the sensor.
Yet another method involves taking samples of a process fluid to determine if the sensors are out of tolerance. If a sample tested in the lab indicates the sensors are inadequate, they may request an offset of the data or that the sensor be replaced. Similarly, one can also use information from a process control system to determine when to remove sensors. If two redundant pH readings are sufficiently far apart at any given point, operations may require the sensors to be replaced. These approaches have significant rates of false positives—a temporary process upset or poor grab sample procedure result in sensors being replaced early and often. This not only increases maintenance and labor costs but also reduces on-site confidence in the pH measurement significantly.
Still another approach involves using temperature and process data from an individual sensor to determine its sensor life. This approach claims many of the same benefits, namely dynamically determining lifespan based on process conditions. However, this approach differs in that it uses a single sensor and attempts to constantly guess remaining sensor life.